def test_compute_lnZ(self):
v = T.matrix('v')
z = T.iscalar('z')
V = cartesian([(0, 1)] * self.input_size, dtype=config.floatX)
#H = cartesian([(0, 1)] * self.hidden_size, dtype=config.floatX)
# We simulate having an infinite number of hidden units by adding lot of hidden units with parameters set to 0.
nb_hidden_units_to_add = 10000
model = iRBM(input_size=self.model.input_size,
hidden_size=self.model.hidden_size + nb_hidden_units_to_add,
beta=self.model.beta.get_value())
model.W.set_value(np.r_[self.model.W.get_value(), np.zeros((nb_hidden_units_to_add, model.input_size), dtype=theano.config.floatX)])
model.b.set_value(np.r_[self.model.b.get_value(), np.zeros((nb_hidden_units_to_add,), dtype=theano.config.floatX)])
model.c.set_value(self.model.c.get_value())
v = T.matrix('v')
z = T.iscalar('z')
F_vz = theano.function([v, z], model.F(v, z))
energies = []
for z in range(1, model.hidden_size+1):
energies.append(F_vz(V, z))
lnZ = logsumexp(-np.array(energies)).eval()
lnZ_using_free_energy = theano.function([v], logsumexp(-self.model.free_energy(v)))
assert_almost_equal(lnZ_using_free_energy(V), lnZ, decimal=5) # decimal=5 needed for float32
def test_verify_AIS(self):
model = iRBM(input_size=self.input_size,
hidden_size=self.hidden_size,
beta=self.beta)
model.W.set_value(self.W)
model.b.set_value(self.b)
model.c.set_value(self.c)
# Brute force
print "Computing lnZ using brute force (i.e. summing the free energy of all posible $v$)..."
V = theano.shared(
value=cartesian([(0, 1)] * self.input_size, dtype=config.floatX))
brute_force_lnZ = logsumexp(-model.free_energy(V), 0)
f_brute_force_lnZ = theano.function([], brute_force_lnZ)
params_bak = [param.get_value() for param in model.parameters]
print "Approximating lnZ using AIS..."
import time
start = time.time()
try:
experiment_path = tempfile.mkdtemp()
result = compute_AIS(model,
M=self.nb_samples,
betas=self.betas,
seed=1234,
experiment_path=experiment_path,
force=True)
logcummean_Z, logcumstd_Z_down, logcumstd_Z_up = result[
'logcummean_Z'], result['logcumstd_Z_down'], result[
'logcumstd_Z_up']
std_lnZ = result['std_lnZ']
print "{0} sec".format(time.time() - start)
import pylab as plt
plt.gca().set_xmargin(0.1)
plt.errorbar(range(1, self.nb_samples + 1),
logcummean_Z,
yerr=[std_lnZ, std_lnZ],
fmt='or')
plt.errorbar(range(1, self.nb_samples + 1),
logcummean_Z,
yerr=[logcumstd_Z_down, logcumstd_Z_up],
fmt='ob')
plt.plot([1, self.nb_samples], [f_brute_force_lnZ()] * 2, '--g')
plt.ticklabel_format(useOffset=False, axis='y')
plt.show()
AIS_logZ = logcummean_Z[-1]
assert_array_equal(params_bak[0], model.W.get_value())
assert_array_equal(params_bak[1], model.b.get_value())
assert_array_equal(params_bak[2], model.c.get_value())
print np.abs(AIS_logZ - f_brute_force_lnZ())
assert_almost_equal(AIS_logZ, f_brute_force_lnZ(), decimal=2)
finally:
shutil.rmtree(experiment_path)
def test_beta(self):
beta = 1.1
model = iRBM(input_size=self.input_size,
#hidden_size=1000,
beta=beta)
rng = np.random.RandomState(42)
v1 = (rng.rand(1, self.input_size) > 0.5).astype(config.floatX)
v = T.matrix('v')
z = T.iscalar('z')
F_vz = theano.function([v, z], model.F(v, z))
# Suppose all parameters of the models have a value of 0 (i.e. l=0), then
# as we add hidden units, $Z(v)=\sum_z exp(-F(v, z))$ should converge to
geometric_ratio = T.exp((1.-model.beta) * T.nnet.softplus(0.)).eval()
log_shifted_geometric_convergence = np.float32(np.log(geometric_ratio / (1. - geometric_ratio)))
Zv_theorical_convergence = log_shifted_geometric_convergence
# In fact, we can estimate the number of hidden units needed to be at $\epsilon$ of the convergence point.
eps = 1e-7
hidden_size = (np.log(eps)+np.log(1-geometric_ratio))/np.log(geometric_ratio)
hidden_size = int(np.ceil(hidden_size))
model.hidden_size = hidden_size
model.W.set_value(np.zeros((model.hidden_size, model.input_size), dtype=theano.config.floatX))
model.b.set_value(np.zeros((model.hidden_size,), dtype=theano.config.floatX))
free_energies = []
for z in range(1, model.hidden_size+1):
free_energies.append(F_vz(v1, z))
Z_v = logsumexp(-np.array(free_energies)).eval()
print hidden_size, ':', Z_v, Zv_theorical_convergence, abs(Zv_theorical_convergence-Z_v)
assert_almost_equal(Z_v, Zv_theorical_convergence, decimal=6)
def test_gradients_auto_vs_manual(self):
rng = np.random.RandomState(42)
batch_size = 5
input_size = 10
model = iRBM(input_size=input_size,
hidden_size=32,
beta=1.01,
CDk=1,
rng=np.random.RandomState(42))
W = rng.rand(model.hidden_size, model.input_size).astype(theano.config.floatX)
model.W = theano.shared(value=W.astype(theano.config.floatX), name='W', borrow=True)
b = rng.rand(model.hidden_size).astype(theano.config.floatX)
model.b = theano.shared(value=b.astype(theano.config.floatX), name='b', borrow=True)
c = rng.rand(model.input_size).astype(theano.config.floatX)
model.c = theano.shared(value=c.astype(theano.config.floatX), name='c', borrow=True)
params = [model.W, model.b, model.c]
chain_start = T.matrix('start')
chain_end = T.matrix('end')
chain_start_value = (rng.rand(batch_size, input_size) > 0.5).astype(theano.config.floatX)
chain_end_value = (rng.rand(batch_size, input_size) > 0.5).astype(theano.config.floatX)
chain_start.tag.test_value = chain_start_value
chain_end.tag.test_value = chain_end_value
### Computing gradients using automatic differentation ###
cost = T.mean(model.free_energy(chain_start)) - T.mean(model.free_energy(chain_end))
gparams_auto = T.grad(cost, params, consider_constant=[chain_end])
### Computing gradients manually ###
h = RBM.sample_h_given_v(model, chain_start, return_probs=True)
_h = RBM.sample_h_given_v(model, chain_end, return_probs=True)
icdf = model.icdf_z_given_v(chain_start)
_icdf = model.icdf_z_given_v(chain_end)
if model.penalty == "softplus_bi":
penalty = model.beta * T.nnet.sigmoid(model.b)
elif self.penalty == "softplus0":
penalty = model.beta * T.nnet.sigmoid(0)
grad_W = (T.dot(chain_end.T, _h*_icdf) - T.dot(chain_start.T, h*icdf)).T / batch_size
grad_b = T.mean((_h-penalty)*_icdf - (h-penalty)*icdf, axis=0)
grad_c = T.mean(chain_end - chain_start, axis=0)
gparams_manual = [grad_W, grad_b, grad_c]
grad_W.name, grad_b.name, grad_c.name = "grad_W", "grad_b", "grad_c"
for gparam_auto, gparam_manual in zip(gparams_auto, gparams_manual):
param1 = gparam_auto.eval({chain_start: chain_start_value, chain_end: chain_end_value})
param2 = gparam_manual.eval({chain_start: chain_start_value, chain_end: chain_end_value})
assert_array_almost_equal(param1, param2, err_msg=gparam_manual.name, decimal=5) # decimal=5 needed for float32
def test_free_energy(self):
v = T.matrix('v')
h = T.matrix('h')
z = T.iscalar('z')
logsumexp_E = theano.function([v, h, z],
-logsumexp(-self.model.E(v, h, z)))
F_vz = theano.function([v, z], self.model.F(v, z))
rng = np.random.RandomState(42)
v1 = (rng.rand(1, self.input_size) > 0.5).astype(config.floatX)
H = cartesian([(0, 1)] * self.hidden_size, dtype=config.floatX)
# Check the free energy F(v, z) is correct.
for z in range(1, self.hidden_size + 1):
h = np.array(H[::2**(self.hidden_size - z)])
free_energy_vz = logsumexp_E(v1, h, z)
assert_almost_equal(F_vz(v1, z), free_energy_vz, decimal=6)
# We now check that free energy F(v) assumes an infinite number of hidden units.
# To do so, we create another model that has an infinite (read a lot) number of hidden units with parameters set to 0.
nb_hidden_units_to_add = 10000
model = iRBM(input_size=self.model.input_size,
hidden_size=self.model.hidden_size +
nb_hidden_units_to_add,
beta=self.model.beta.get_value())
model.W.set_value(np.r_[self.model.W.get_value(),
np.zeros(
(nb_hidden_units_to_add, model.input_size),
dtype=theano.config.floatX)])
model.b.set_value(
np.r_[self.model.b.get_value(),
np.zeros(
(nb_hidden_units_to_add, ), dtype=theano.config.floatX)])
model.c.set_value(self.model.c.get_value())
v = T.matrix('v')
z = T.iscalar('z')
F_vz = theano.function([v, z], model.F(v, z))
free_energies_vz = []
for z in range(1, model.hidden_size + 1):
free_energies_vz.append(F_vz(v1, z))
Fv = -logsumexp(-np.array(free_energies_vz)).eval()
v = T.matrix('v')
free_energy = theano.function([v], self.model.free_energy(v))
assert_array_almost_equal(free_energy(v1), [Fv],
decimal=5) # decimal=5 needed for float32
v2 = np.tile(v1, (self.batch_size, 1))
assert_array_almost_equal(free_energy(v2), [Fv] * self.batch_size,
decimal=5) # decimal=5 needed for float32
def test_base_rate(self):
# All binary combinaisons for V and H_z
V = cartesian([(0, 1)] * self.input_size, dtype=config.floatX)
#H = cartesian([(0, 1)] * self.hidden_size, dtype=config.floatX)
base_rates = []
# Add the uniform base rate, i.e. all parameters of the model are set to 0.
base_rates.append(self.model.get_base_rate())
# Add the base rate where visible biases are the ones from the model.
base_rates.append(self.model.get_base_rate('c'))
# Add the base rate where hidden biases are the ones from the model.
# base_rates.append(self.model.get_base_rate('b')) # Not implemented
for base_rate, anneable_params in base_rates:
print base_rate
base_rate_lnZ = base_rate.compute_lnZ().eval().astype(
config.floatX)
# We simulate having an infinite number of hidden units by adding lot of hidden units with parameters set to 0.
nb_hidden_units_to_add = 10000
model = iRBM(input_size=base_rate.input_size,
hidden_size=base_rate.hidden_size +
nb_hidden_units_to_add,
beta=base_rate.beta.get_value())
model.W = T.join(
0, base_rate.W,
np.zeros((nb_hidden_units_to_add, model.input_size),
dtype=theano.config.floatX))
model.b = T.join(
0, base_rate.b,
np.zeros((nb_hidden_units_to_add, ),
dtype=theano.config.floatX))
model.c = base_rate.c
v = T.matrix('v')
z = T.iscalar('z')
F_vz = theano.function([v, z], model.F(v, z))
energies = []
for z in range(1, model.hidden_size + 1):
energies.append(F_vz(V, z))
brute_force_lnZ = logsumexp(-np.array(energies)).eval()
assert_almost_equal(brute_force_lnZ.astype(config.floatX),
base_rate_lnZ,
decimal=5)
theano_lnZ = logsumexp(-base_rate.free_energy(V), axis=0).eval()
assert_almost_equal(theano_lnZ.astype(config.floatX),
base_rate_lnZ,
decimal=6)
def test_sample_z_given_v(self):
v = T.matrix('v')
z = T.iscalar('z')
v1 = np.random.rand(1, self.input_size).astype(config.floatX)
# We simulate having an infinite number of hidden units by adding lot of hidden units with parameters set to 0.
nb_hidden_units_to_add = 10000
model = iRBM(input_size=self.model.input_size,
hidden_size=self.model.hidden_size + nb_hidden_units_to_add,
beta=self.model.beta.get_value())
model.W.set_value(np.r_[self.model.W.get_value(), np.zeros((nb_hidden_units_to_add, model.input_size), dtype=theano.config.floatX)])
model.b.set_value(np.r_[self.model.b.get_value(), np.zeros((nb_hidden_units_to_add,), dtype=theano.config.floatX)])
model.c.set_value(self.model.c.get_value())
v = T.matrix('v')
z = T.iscalar('z')
F_vz = theano.function([v, z], model.F(v, z))
energies = []
for z in range(1, model.hidden_size+1):
energies.append(F_vz(v1, z))
energies = np.array(energies).T
neg_log_probs = energies - -logsumexp(-energies, axis=1).eval()
probs = np.exp(-neg_log_probs)
expected_icdf = np.cumsum(probs[:, ::-1], axis=1)[:, ::-1]
expected_icdf = expected_icdf[:, :self.model.hidden_size]
# Test inverse cdf
v = T.matrix('v')
icdf_z_given_v = theano.function([v], self.model.icdf_z_given_v(v))
assert_array_almost_equal(icdf_z_given_v(v1), expected_icdf, decimal=5) # decimal=5 needed for float32
batch_size = 500000
self.model.batch_size = batch_size
sample_zmask_given_v = theano.function([v], self.model.sample_zmask_given_v(v))
v2 = np.tile(v1, (self.model.batch_size, 1))
#theano.printing.pydotprint(sample_zmask_given_v)
z_mask = sample_zmask_given_v(v2)
# First hidden units should always be considered i.e. z_mask[:, 0] == 1
assert_equal(np.sum(z_mask[:, 0] == 0, axis=0), 0)
# Test that sampled masks are as expected i.e. equal expected_icdf
freq_per_z = np.sum(z_mask, axis=0) / self.model.batch_size
assert_array_almost_equal(freq_per_z, expected_icdf[0], decimal=3, err_msg="Tested using MC sampling, rerun it to be certain that is an error or increase 'batch_size'.")
def test_verify_AIS(self):
model = iRBM(input_size=self.input_size,
hidden_size=self.hidden_size,
beta=self.beta)
model.W.set_value(self.W)
model.b.set_value(self.b)
model.c.set_value(self.c)
# Brute force
print "Computing lnZ using brute force (i.e. summing the free energy of all posible $v$)..."
V = theano.shared(value=cartesian([(0, 1)] * self.input_size, dtype=config.floatX))
brute_force_lnZ = logsumexp(-model.free_energy(V), 0)
f_brute_force_lnZ = theano.function([], brute_force_lnZ)
params_bak = [param.get_value() for param in model.parameters]
print "Approximating lnZ using AIS..."
import time
start = time.time()
try:
ais_working_dir = tempfile.mkdtemp()
result = compute_AIS(model, M=self.nb_samples, betas=self.betas, seed=1234, ais_working_dir=ais_working_dir, force=True)
logcummean_Z, logcumstd_Z_down, logcumstd_Z_up = result['logcummean_Z'], result['logcumstd_Z_down'], result['logcumstd_Z_up']
std_lnZ = result['std_lnZ']
print "{0} sec".format(time.time() - start)
import pylab as plt
plt.gca().set_xmargin(0.1)
plt.errorbar(range(1, self.nb_samples+1), logcummean_Z, yerr=[std_lnZ, std_lnZ], fmt='or')
plt.errorbar(range(1, self.nb_samples+1), logcummean_Z, yerr=[logcumstd_Z_down, logcumstd_Z_up], fmt='ob')
plt.plot([1, self.nb_samples], [f_brute_force_lnZ()]*2, '--g')
plt.ticklabel_format(useOffset=False, axis='y')
plt.show()
AIS_logZ = logcummean_Z[-1]
assert_array_equal(params_bak[0], model.W.get_value())
assert_array_equal(params_bak[1], model.b.get_value())
assert_array_equal(params_bak[2], model.c.get_value())
print np.abs(AIS_logZ - f_brute_force_lnZ())
assert_almost_equal(AIS_logZ, f_brute_force_lnZ(), decimal=2)
finally:
shutil.rmtree(ais_working_dir)
def test_free_energy(self):
v = T.matrix('v')
h = T.matrix('h')
z = T.iscalar('z')
logsumexp_E = theano.function([v, h, z], -logsumexp(-self.model.E(v, h, z)))
F_vz = theano.function([v, z], self.model.F(v, z))
rng = np.random.RandomState(42)
v1 = (rng.rand(1, self.input_size) > 0.5).astype(config.floatX)
H = cartesian([(0, 1)] * self.hidden_size, dtype=config.floatX)
# Check the free energy F(v, z) is correct.
for z in range(1, self.hidden_size+1):
h = np.array(H[::2**(self.hidden_size-z)])
free_energy_vz = logsumexp_E(v1, h, z)
assert_almost_equal(F_vz(v1, z), free_energy_vz, decimal=6)
# We now check that free energy F(v) assumes an infinite number of hidden units.
# To do so, we create another model that has an infinite (read a lot) number of hidden units with parameters set to 0.
nb_hidden_units_to_add = 10000
model = iRBM(input_size=self.model.input_size,
hidden_size=self.model.hidden_size + nb_hidden_units_to_add,
beta=self.model.beta.get_value())
model.W.set_value(np.r_[self.model.W.get_value(), np.zeros((nb_hidden_units_to_add, model.input_size), dtype=theano.config.floatX)])
model.b.set_value(np.r_[self.model.b.get_value(), np.zeros((nb_hidden_units_to_add,), dtype=theano.config.floatX)])
model.c.set_value(self.model.c.get_value())
v = T.matrix('v')
z = T.iscalar('z')
F_vz = theano.function([v, z], model.F(v, z))
free_energies_vz = []
for z in range(1, model.hidden_size+1):
free_energies_vz.append(F_vz(v1, z))
Fv = -logsumexp(-np.array(free_energies_vz)).eval()
v = T.matrix('v')
free_energy = theano.function([v], self.model.free_energy(v))
assert_array_almost_equal(free_energy(v1), [Fv], decimal=5) # decimal=5 needed for float32
v2 = np.tile(v1, (self.batch_size, 1))
assert_array_almost_equal(free_energy(v2), [Fv]*self.batch_size, decimal=5) # decimal=5 needed for float32
def setUp(self):
self.input_size = 4
self.hidden_size = 7
self.beta = 1.01
self.batch_size = 100
rng = np.random.RandomState(42)
self.W = rng.randn(self.hidden_size, self.input_size).astype(config.floatX)
self.b = rng.randn(self.hidden_size).astype(config.floatX)
self.c = rng.randn(self.input_size).astype(config.floatX)
self.model = iRBM(input_size=self.input_size,
hidden_size=self.hidden_size,
beta=self.beta)
self.model.W.set_value(self.W)
self.model.b.set_value(self.b)
self.model.c.set_value(self.c)
def setUp(self):
self.input_size = 4
self.hidden_size = 7
self.beta = 1.01
self.batch_size = 100
rng = np.random.RandomState(42)
self.W = rng.randn(self.hidden_size,
self.input_size).astype(config.floatX)
self.b = rng.randn(self.hidden_size).astype(config.floatX)
self.c = rng.randn(self.input_size).astype(config.floatX)
self.model = iRBM(input_size=self.input_size,
hidden_size=self.hidden_size,
beta=self.beta)
self.model.W.set_value(self.W)
self.model.b.set_value(self.b)
self.model.c.set_value(self.c)
def test_beta(self):
beta = 1.1
model = iRBM(
input_size=self.input_size,
#hidden_size=1000,
beta=beta)
rng = np.random.RandomState(42)
v1 = (rng.rand(1, self.input_size) > 0.5).astype(config.floatX)
v = T.matrix('v')
z = T.iscalar('z')
F_vz = theano.function([v, z], model.F(v, z))
# Suppose all parameters of the models have a value of 0 (i.e. l=0), then
# as we add hidden units, $Z(v)=\sum_z exp(-F(v, z))$ should converge to
geometric_ratio = T.exp((1. - model.beta) * T.nnet.softplus(0.)).eval()
log_shifted_geometric_convergence = np.float32(
np.log(geometric_ratio / (1. - geometric_ratio)))
Zv_theorical_convergence = log_shifted_geometric_convergence
# In fact, we can estimate the number of hidden units needed to be at $\epsilon$ of the convergence point.
eps = 1e-7
hidden_size = (np.log(eps) +
np.log(1 - geometric_ratio)) / np.log(geometric_ratio)
hidden_size = int(np.ceil(hidden_size))
model.hidden_size = hidden_size
model.W.set_value(
np.zeros((model.hidden_size, model.input_size),
dtype=theano.config.floatX))
model.b.set_value(
np.zeros((model.hidden_size, ), dtype=theano.config.floatX))
free_energies = []
for z in range(1, model.hidden_size + 1):
free_energies.append(F_vz(v1, z))
Z_v = logsumexp(-np.array(free_energies)).eval()
print hidden_size, ':', Z_v, Zv_theorical_convergence, abs(
Zv_theorical_convergence - Z_v)
assert_almost_equal(Z_v, Zv_theorical_convergence, decimal=6)
def test_base_rate(self):
# All binary combinaisons for V and H_z
V = cartesian([(0, 1)] * self.input_size, dtype=config.floatX)
#H = cartesian([(0, 1)] * self.hidden_size, dtype=config.floatX)
base_rates = []
# Add the uniform base rate, i.e. all parameters of the model are set to 0.
base_rates.append(self.model.get_base_rate())
# Add the base rate where visible biases are the ones from the model.
base_rates.append(self.model.get_base_rate('c'))
# Add the base rate where hidden biases are the ones from the model.
# base_rates.append(self.model.get_base_rate('b')) # Not implemented
for base_rate, anneable_params in base_rates:
print base_rate
base_rate_lnZ = base_rate.compute_lnZ().eval().astype(config.floatX)
# We simulate having an infinite number of hidden units by adding lot of hidden units with parameters set to 0.
nb_hidden_units_to_add = 10000
model = iRBM(input_size=base_rate.input_size,
hidden_size=base_rate.hidden_size + nb_hidden_units_to_add,
beta=base_rate.beta.get_value())
model.W = T.join(0, base_rate.W, np.zeros((nb_hidden_units_to_add, model.input_size), dtype=theano.config.floatX))
model.b = T.join(0, base_rate.b, np.zeros((nb_hidden_units_to_add,), dtype=theano.config.floatX))
model.c = base_rate.c
v = T.matrix('v')
z = T.iscalar('z')
F_vz = theano.function([v, z], model.F(v, z))
energies = []
for z in range(1, model.hidden_size+1):
energies.append(F_vz(V, z))
brute_force_lnZ = logsumexp(-np.array(energies)).eval()
assert_almost_equal(brute_force_lnZ.astype(config.floatX), base_rate_lnZ, decimal=5)
theano_lnZ = logsumexp(-base_rate.free_energy(V), axis=0).eval()
assert_almost_equal(theano_lnZ.astype(config.floatX), base_rate_lnZ, decimal=6)
def test_compute_lnZ(self):
v = T.matrix('v')
z = T.iscalar('z')
V = cartesian([(0, 1)] * self.input_size, dtype=config.floatX)
#H = cartesian([(0, 1)] * self.hidden_size, dtype=config.floatX)
# We simulate having an infinite number of hidden units by adding lot of hidden units with parameters set to 0.
nb_hidden_units_to_add = 10000
model = iRBM(input_size=self.model.input_size,
hidden_size=self.model.hidden_size +
nb_hidden_units_to_add,
beta=self.model.beta.get_value())
model.W.set_value(np.r_[self.model.W.get_value(),
np.zeros(
(nb_hidden_units_to_add, model.input_size),
dtype=theano.config.floatX)])
model.b.set_value(
np.r_[self.model.b.get_value(),
np.zeros(
(nb_hidden_units_to_add, ), dtype=theano.config.floatX)])
model.c.set_value(self.model.c.get_value())
v = T.matrix('v')
z = T.iscalar('z')
F_vz = theano.function([v, z], model.F(v, z))
energies = []
for z in range(1, model.hidden_size + 1):
energies.append(F_vz(V, z))
lnZ = logsumexp(-np.array(energies)).eval()
lnZ_using_free_energy = theano.function(
[v], logsumexp(-self.model.free_energy(v)))
assert_almost_equal(lnZ_using_free_energy(V), lnZ,
decimal=5) # decimal=5 needed for float32
def model_factory(model_name, input_size, hyperparams):
#Set learning rate method that will be used.
if hyperparams["ConstantLearningRate"] is not None:
infos = hyperparams["ConstantLearningRate"].split()
lr = float(infos[0])
lr_method = ConstantLearningRate(lr=lr)
elif hyperparams["ADAGRAD"] is not None:
infos = hyperparams["ADAGRAD"].split()
lr = float(infos[0])
eps = float(infos[1]) if len(infos) > 1 else 1e-6
lr_method = ADAGRAD(lr=lr, eps=eps)
else:
raise ValueError("The update rule is mandatory!")
#Set regularization method that will be used.
regularization_method = NoRegularization()
if hyperparams["L1Regularization"] is not None and hyperparams["L1Regularization"] != 0:
lambda_factor = float(hyperparams["L1Regularization"])
regularization_method = L1Regularization(lambda_factor)
elif hyperparams["L2Regularization"] is not None and hyperparams["L2Regularization"] != 0:
lambda_factor = float(hyperparams["L2Regularization"])
regularization_method = L2Regularization(lambda_factor)
#Set contrastive divergence method to use.
CD_method = ContrastiveDivergence()
if hyperparams["PCD"]:
CD_method = PersistentCD(input_size, nb_particles=hyperparams['batch_size'])
rng = np.random.RandomState(hyperparams["seed"])
#Build model
if model_name == "rbm":
from iRBM.models.rbm import RBM
model = RBM(input_size=input_size,
hidden_size=hyperparams["size"],
learning_rate=lr_method,
regularization=regularization_method,
CD=CD_method,
CDk=hyperparams["cdk"],
rng=rng
)
elif model_name == "orbm":
from iRBM.models.orbm import oRBM
model = oRBM(input_size=input_size,
hidden_size=hyperparams["size"],
beta=hyperparams["beta"],
learning_rate=lr_method,
regularization=regularization_method,
CD=CD_method,
CDk=hyperparams["cdk"],
rng=rng
)
elif model_name == "irbm":
from iRBM.models.irbm import iRBM
model = iRBM(input_size=input_size,
hidden_size=hyperparams["size"],
beta=hyperparams["beta"],
learning_rate=lr_method,
regularization=regularization_method,
CD=CD_method,
CDk=hyperparams["cdk"],
rng=rng
)
return model
def test_gradients_auto_vs_manual(self):
rng = np.random.RandomState(42)
batch_size = 5
input_size = 10
model = iRBM(input_size=input_size,
hidden_size=32,
beta=1.01,
CDk=1,
rng=np.random.RandomState(42))
W = rng.rand(model.hidden_size,
model.input_size).astype(theano.config.floatX)
model.W = theano.shared(value=W.astype(theano.config.floatX),
name='W',
borrow=True)
b = rng.rand(model.hidden_size).astype(theano.config.floatX)
model.b = theano.shared(value=b.astype(theano.config.floatX),
name='b',
borrow=True)
c = rng.rand(model.input_size).astype(theano.config.floatX)
model.c = theano.shared(value=c.astype(theano.config.floatX),
name='c',
borrow=True)
params = [model.W, model.b, model.c]
chain_start = T.matrix('start')
chain_end = T.matrix('end')
chain_start_value = (rng.rand(batch_size, input_size) > 0.5).astype(
theano.config.floatX)
chain_end_value = (rng.rand(batch_size, input_size) > 0.5).astype(
theano.config.floatX)
chain_start.tag.test_value = chain_start_value
chain_end.tag.test_value = chain_end_value
### Computing gradients using automatic differentation ###
cost = T.mean(model.free_energy(chain_start)) - T.mean(
model.free_energy(chain_end))
gparams_auto = T.grad(cost, params, consider_constant=[chain_end])
### Computing gradients manually ###
h = RBM.sample_h_given_v(model, chain_start, return_probs=True)
_h = RBM.sample_h_given_v(model, chain_end, return_probs=True)
icdf = model.icdf_z_given_v(chain_start)
_icdf = model.icdf_z_given_v(chain_end)
if model.penalty == "softplus_bi":
penalty = model.beta * T.nnet.sigmoid(model.b)
elif self.penalty == "softplus0":
penalty = model.beta * T.nnet.sigmoid(0)
grad_W = (T.dot(chain_end.T, _h * _icdf) -
T.dot(chain_start.T, h * icdf)).T / batch_size
grad_b = T.mean((_h - penalty) * _icdf - (h - penalty) * icdf, axis=0)
grad_c = T.mean(chain_end - chain_start, axis=0)
gparams_manual = [grad_W, grad_b, grad_c]
grad_W.name, grad_b.name, grad_c.name = "grad_W", "grad_b", "grad_c"
for gparam_auto, gparam_manual in zip(gparams_auto, gparams_manual):
param1 = gparam_auto.eval({
chain_start: chain_start_value,
chain_end: chain_end_value
})
param2 = gparam_manual.eval({
chain_start: chain_start_value,
chain_end: chain_end_value
})
assert_array_almost_equal(
param1, param2, err_msg=gparam_manual.name,
decimal=5) # decimal=5 needed for float32
def model_factory(model_name, input_size, hyperparams):
#Set learning rate method that will be used.
if hyperparams["ConstantLearningRate"] is not None:
infos = hyperparams["ConstantLearningRate"].split()
lr = float(infos[0])
lr_method = ConstantLearningRate(lr=lr)
elif hyperparams["ADAGRAD"] is not None:
infos = hyperparams["ADAGRAD"].split()
lr = float(infos[0])
eps = float(infos[1]) if len(infos) > 1 else 1e-6
lr_method = ADAGRAD(lr=lr, eps=eps)
else:
raise ValueError("The update rule is mandatory!")
#Set regularization method that will be used.
regularization_method = NoRegularization()
if hyperparams["L1Regularization"] is not None and hyperparams[
"L1Regularization"] != 0:
lambda_factor = float(hyperparams["L1Regularization"])
regularization_method = L1Regularization(lambda_factor)
elif hyperparams["L2Regularization"] is not None and hyperparams[
"L2Regularization"] != 0:
lambda_factor = float(hyperparams["L2Regularization"])
regularization_method = L2Regularization(lambda_factor)
#Set contrastive divergence method to use.
CD_method = ContrastiveDivergence()
if hyperparams["PCD"]:
CD_method = PersistentCD(input_size,
nb_particles=hyperparams['batch_size'])
rng = np.random.RandomState(hyperparams["seed"])
#Build model
if model_name == "rbm":
from iRBM.models.rbm import RBM
model = RBM(input_size=input_size,
hidden_size=hyperparams["size"],
learning_rate=lr_method,
regularization=regularization_method,
CD=CD_method,
CDk=hyperparams["cdk"],
rng=rng)
elif model_name == "orbm":
from iRBM.models.orbm import oRBM
model = oRBM(input_size=input_size,
hidden_size=hyperparams["size"],
beta=hyperparams["beta"],
learning_rate=lr_method,
regularization=regularization_method,
CD=CD_method,
CDk=hyperparams["cdk"],
rng=rng)
elif model_name == "irbm":
from iRBM.models.irbm import iRBM
model = iRBM(input_size=input_size,
hidden_size=hyperparams["size"],
beta=hyperparams["beta"],
learning_rate=lr_method,
regularization=regularization_method,
CD=CD_method,
CDk=hyperparams["cdk"],
rng=rng)
return model
def test_sample_z_given_v(self):
v = T.matrix('v')
z = T.iscalar('z')
v1 = np.random.rand(1, self.input_size).astype(config.floatX)
# We simulate having an infinite number of hidden units by adding lot of hidden units with parameters set to 0.
nb_hidden_units_to_add = 10000
model = iRBM(input_size=self.model.input_size,
hidden_size=self.model.hidden_size +
nb_hidden_units_to_add,
beta=self.model.beta.get_value())
model.W.set_value(np.r_[self.model.W.get_value(),
np.zeros(
(nb_hidden_units_to_add, model.input_size),
dtype=theano.config.floatX)])
model.b.set_value(
np.r_[self.model.b.get_value(),
np.zeros(
(nb_hidden_units_to_add, ), dtype=theano.config.floatX)])
model.c.set_value(self.model.c.get_value())
v = T.matrix('v')
z = T.iscalar('z')
F_vz = theano.function([v, z], model.F(v, z))
energies = []
for z in range(1, model.hidden_size + 1):
energies.append(F_vz(v1, z))
energies = np.array(energies).T
neg_log_probs = energies - -logsumexp(-energies, axis=1).eval()
probs = np.exp(-neg_log_probs)
expected_icdf = np.cumsum(probs[:, ::-1], axis=1)[:, ::-1]
expected_icdf = expected_icdf[:, :self.model.hidden_size]
# Test inverse cdf
v = T.matrix('v')
icdf_z_given_v = theano.function([v], self.model.icdf_z_given_v(v))
assert_array_almost_equal(icdf_z_given_v(v1), expected_icdf,
decimal=5) # decimal=5 needed for float32
batch_size = 500000
self.model.batch_size = batch_size
sample_zmask_given_v = theano.function(
[v], self.model.sample_zmask_given_v(v))
v2 = np.tile(v1, (self.model.batch_size, 1))
#theano.printing.pydotprint(sample_zmask_given_v)
z_mask = sample_zmask_given_v(v2)
# First hidden units should always be considered i.e. z_mask[:, 0] == 1
assert_equal(np.sum(z_mask[:, 0] == 0, axis=0), 0)
# Test that sampled masks are as expected i.e. equal expected_icdf
freq_per_z = np.sum(z_mask, axis=0) / self.model.batch_size
assert_array_almost_equal(
freq_per_z,
expected_icdf[0],
decimal=3,
err_msg=
"Tested using MC sampling, rerun it to be certain that is an error or increase 'batch_size'."
)
